Catalytic converters in cars. A quadratic model was applied to motor vehicle toxic emissions data collected in Mexico City (Environmental Science & Engineering, Sept. 1, 2000). The following equation was used to predict the percentage (y) of motor vehicles without catalytic converters in the Mexico City fleet for a given year (x): β^2

a. Explain why the valueβ^0=325790has no practical interpretation.

b. Explain why the valueβ^1=-321.67should not be Interpreted as a slope.

c. Examine the value ofβ^2to determine the nature of the curvature (upward or downward) in the sample data.

d. The researchers used the model to estimate “that just after the year 2021 the fleet of cars with catalytic converters will completely disappear.” Comment on the danger of using the model to predict y in the year 2021. (Note: The model was fit to data collected between 1984 and 1999.)

Short Answer

Expert verified

a. Since, our x variable is a time concept it cannot be zero, hence, we cannot practically interpret the value.

b. The percentage of motor vehicles (y) without catalytic converters for a given year (x) is predicted here. When x goes up by 1-unit, y according to the question decreases by 321.67 units. But since we are measuring y in percentage form this number is not reliable and thus should not be interpreted as a slope.

c. The value ofβ^2 is coming out to be 0.0794. A positive value here denotes that the curve is upward sloping.

d. The model was fit to data collected between 1984 and 1999. The researchers want to use the regression equation to predict y in the year 2021. Using the model to predict y in the year 2021 will not give very accurate results as almost 20 years have passed and the variables and their relationship with y changes over due period of time.

Step by step solution

01

Interpretation of β0

Here, the value of β^1is 325,790. β0represent the y-intercept and the value here 325,790 denotes no of motor vehicles without catalytic converters in the Mexico City fleet for a given year. Since, our x variable is a time concept it cannot be zero, hence, we cannot practically interpret the value.

02

Simplification of β1

The percentage of motor vehicles (y) without catalytic converters for a given year (x) is predicted here. When x goes up by 1-unit, y according to the question decreases by 321.67 units. But since we are measuring y in percentage form this number is not reliable and thus should not be interpreted as a slope.

03

Clarification of β2

The value ofβ^2 is coming out to be 0.0794. A positive value here denotes that the curve is upward sloping.

04

Prediction

The model was fit to data collected between 1984 and 1999. The researchers want to use the regression equation to predict y in the year 2021. Using the model to predict y in the year 2021 will not give very accurate results as almost 20 years have passed and the variables and their relationship with y changes overdue period of time.

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Most popular questions from this chapter

Question:If the analysis of variance F-test leads to the conclusion that at least one of the model parameters is nonzero, can you conclude that the model is the best predictor for the dependent variable ? Can you conclude that all of the terms in the model are important for predicting ? What is the appropriate conclusion?

Suppose you fit the regression model Ey=β0+β1x1+β2x2+β3x22+β4x1x2+β5x1x222 to n = 35 data points and wish to test the null hypothesis H0:β4=β5=0

  1. State the alternative hypothesis.

  2. Explain in detail how to compute the F-statistic needed to test the null hypothesis.

  3. What are the numerator and denominator degrees of freedom associated with the F-statistic in part b?

  4. Give the rejection region for the test if α = .05.

Question:How is the number of degrees of freedom available for estimating σ2(the variance ofε ) related to the number of independent variables in a regression model?


Factors that impact an auditor’s judgment. A study was conducted to determine the effects of linguistic delivery style and client credibility on auditors’ judgments (Advances in Accounting and Behavioural Research, 2004). Two hundred auditors from Big 5 accounting firms were each asked to perform an analytical review of a fictitious client’s financial statement. The researchers gave the auditors different information on the client’s credibility and linguistic delivery style of the client’s explanation. Each auditor then provided an assessment of the likelihood that the client-provided explanation accounted for the fluctuation in the financial statement. The three variables of interest—credibility (x1), linguistic delivery style (x2) , and likelihood (y) —were all measured on a numerical scale. Regression analysis was used to fit the interaction model,y=β0+β1x1+β2x2+β3x1x2+ε . The results are summarized in the table at the bottom of page.

a) Interpret the phrase client credibility and linguistic delivery style interact in the words of the problem.

b) Give the null and alternative hypotheses for testing the overall adequacy of the model.

c) Conduct the test, part b, using the information in the table.

d) Give the null and alternative hypotheses for testing whether client credibility and linguistic delivery style interact.

e) Conduct the test, part d, using the information in the table.

f) The researchers estimated the slope of the likelihood–linguistic delivery style line at a low level of client credibility 1x1 = 222. Obtain this estimate and interpret it in the words of the problem.

g) The researchers also estimated the slope of the likelihood–linguistic delivery style line at a high level of client credibility 1x1 = 462. Obtain this estimate and interpret it in the words of the problem.

The first-order model E(y)=β0+β1x1was fit to n = 19 data points. A residual plot for the model is provided below. Is the need for a quadratic term in the model evident from the residual plot? Explain.


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